Patient-Specific Tumor Growth Trajectories Determine Persistent and Resistant Cancer Cell Populations during Treatment with Targeted Therapies

Abstract

The importance of preexisting versus acquired drug resistance in patients with cancer treated with small-molecule tyrosine kinase inhibitors (TKI) remains controversial. The goal of this study is to provide a general estimate of the size and dynamics of a preexisting, drug-resistant tumor cell population versus a slow-growing persister population that is the precursor of acquired TKI resistance. We describe a general model of resistance development, including persister evolution and preexisting resistance, solely based on the macroscopic trajectory of tumor burden during treatment. We applied the model to 20 tumor volume trajectories of EGFR-mutant lung cancer patients treated with the TKI erlotinib. Under the assumption of only preexisting resistant cells or only persister evolution, it is not possible to explain the observed tumor trajectories with realistic parameter values. Assuming only persister evolution would require very high mutation induction rates, while only preexisting resistance would lead to very large preexisting populations of resistant cells at the initiation of treatment. However, combining preexisting resistance with persister populations can explain the observed tumor volume trajectories and yields an estimated preexisting resistant fraction varying from 10 À4 to 10 À1 at the time of treatment initiation for this study cohort. Our results also demonstrate that the growth rate of the resistant population is highly correlated to the time to tumor progression. These estimates of the size of the resistant and persistent tumor cell population during TKI treatment can inform combination treatment strategies such as multi-agent schedules or a combination of targeted agents and radiotherapy.Significance: These findings quantify pre-existing resistance and persister cell populations, which are essential for the integration of targeted agents into the management of locally advanced disease and the timing of radiotherapy in metastatic patients.

Understanding the effect of measurement time on drug characterization

“…Experimental studies show more mixed results [9,[13][14][15][16][17], sometimes showing a decrease in IC 50 with measurement time [9,[13][14][15][16][17], sometimes showing an increase in IC 50 with measurement time [14,15,17], but also exhibiting more complex trends [13]. These discrepancies in model predictions indicate that much more work is needed to identify which models most accurately describe tumor growth [38,64] since these growth models are increasingly being used to guide patient treatment decisions [65].…”

Outgrowth of erlotinib-resistant subpopulations recapitulated in patient-derived lung tumor spheroids and organoids

“…Four cultures (Tumors [11][12][13][14] were maintained for 24 weeks. Tumor 11 was passaged after Week 3 and Week 10 imaging, whereas tumors 12, 13, and 14 were passaged only after Week 10 imaging.…”

Modeling Resistance and Recurrence Patterns of Combined Targeted-Chemoradiotherapy Predicts Benefit of Shorter Induction Period

“…The model also assumed that the growth of the persistent compartment was slowed during TKI administration as seen clinically (Eq. 13) (11,44). The first order pharmacokinetic model of chemotherapy ( -, Eq.…”

“…12) and TKI plasma concentrations ( ./0 , Eq. 14) used in our model were fully described in previous publications (11,21). The state equations were implemented with 0.1 day resolution (∆ ) with the differential effect of each term calculated independently.…”